Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds meaningful context beyond that: the default model is free ('Workers AI Llama-3.3-70b (free)'), passing _apiKey has cost implications ('you pay Anthropic directly'), and it describes the return structure. It doesn't cover failure modes or rate limits, but adds enough for an agent to understand the tool's external behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each earning its place: first sentence explains the core action and output, second adds default and cost behavior, third lists use cases. It is front-loaded with the most important information and avoids fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description explicitly lists the return shape: 'per-model {score, confidence, signals, raw_response} + a combined view.' With annotations covering safety and external access, and full schema coverage, the description is complete for selection and invocation. It also covers use cases and invocation details, so no critical gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds a key semantic detail not in the schema: the default model is 'Workers AI Llama-3.3-70b (free)' and that _apiKey is for 'Anthropic' with a BYO-key cost model. It also clarifies the relationship between models and _apiKey. This goes beyond what the parameter descriptions alone provide, justifying a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It specifies the verb, resource, and scope, and mentions specific models and output. However, it does not explicitly differentiate from sibling tools like scan_competitor_ai_presence, so it's one step below the strongest purpose clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies when to pass _apiKey to probe Anthropic, and notes the free default. However, it does not mention when not to use the tool or name alternative tools, so it lacks the 'when-not' dimension.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.